Myocardial ischemia detection using Hidden Markov principal component analysis
Journal Article
This paper introduces a new temporal version of Principal Component Analysis by using a Hidden Markov Model in order to obtain optimized representations of observed data through time. The novelty of the proposed method consists mainly in the way in which a static dimensionality reduction technique has been combined with a classic mixture model in time, to enhance the capabilities of dimensionality reduction and classification of myocardial ischemia data. Experimental results show improvements in classification accuracies even with highly reduced representations. © Springer-Verlag Berlin Heidelberg 2007.
Full Text
Duke Authors
Cited Authors
- Alvarez López, MA; Henao, R; Orozco, A
Published Date
- January 1, 2008
Published In
Volume / Issue
- 18 /
Start / End Page
- 99 - 103
International Standard Serial Number (ISSN)
- 1680-0737
Digital Object Identifier (DOI)
- 10.1007/978-3-540-74471-9_24
Citation Source
- Scopus